docstr: Infer the threshold for the SVM detector.
  
  The threshold is computed so that the outlier detector would incorrectly classify `fpr` proportion of the
  reference data as outliers.
  
  Parameters
  ----------
  x
      Reference data used to infer the threshold.
  fpr
      False positive rate used to infer the threshold. The false positive rate is the proportion of
      instances in `x` that are incorrectly classified as outliers. The false positive rate should
      be in the range ``(0, 1)``.
  
  Raises
  ------
  ValueError
      Raised if `fpr` is not in ``(0, 1)``.
  NotFittedError
      If called before detector has been fit.
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code: def infer_threshold(self, x: np.ndarray, fpr: float) -> None:
      """Infer the threshold for the SVM detector.
  
      The threshold is computed so that the outlier detector would incorrectly classify `fpr` proportion of the
      reference data as outliers.
  
      Parameters
      ----------
      x
          Reference data used to infer the threshold.
      fpr
          False positive rate used to infer the threshold. The false positive rate is the proportion of
          instances in `x` that are incorrectly classified as outliers. The false positive rate should
          be in the range ``(0, 1)``.
  
      Raises
      ------
      ValueError
          Raised if `fpr` is not in ``(0, 1)``.
      NotFittedError
          If called before detector has been fit.
      """
      self.backend.infer_threshold(
          self.backend._to_backend_dtype(x), fpr
      )
  
file_path: alibi-detect/alibi_detect/od/_svm.py
imports: from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple
  import numpy
  from alibi_detect.base import BaseDetector, FitMixin, ThresholdMixin, outlier_prediction_dict
  from alibi_detect.exceptions import _catch_error
  from alibi_detect.od.pytorch import SgdSVMTorch, BgdSVMTorch
  from alibi_detect.utils._types import Literal
  from alibi_detect.utils.frameworks import BackendValidator
  from alibi_detect.version import __version__
  from alibi_detect.utils._types import TorchDeviceType
label_desc: Use image node_id 4 for calling the SVM obj's underlying member method code with example usage: obj.infer_threshold(x, fpr) without return types